Contents of this book - WSEAS

More documents

Recommendations

Info

Mathematical Methods for Information Science and Economics Plenary Lecture 4 Reduction Method for the Solution of Weakly Singular Integro-Differential Equations Professor Iurie Caraus Faculty of Mathematics and Informatics Moldova State University Chisinau, MOLDOVA E-mail: ciurie.caraus@gmail.com Abstract: Approximation of functions of a complex variable by various finite-dimensional aggregates is an important problem not only in constructive function theory and approximation but also in the justification of direct approximate methods for functional equations. This problem has been well studied for the case of functions defined on standard contours (a straight line segment, the unit circle, and so on). In the case of an arbitrary closed smooth contour Γ in the complex plane, the problem is less studied. It should be noted that conformal mapping from the arbitrary smooth closed contours to the unit circle does not solve the problem. Moreover, it makes more difficulties: • The coefficients, kernel and right part of the transformed equation lose their smoothness; • The power of smoothness appears in convergence speed of collocation method. So that the evaluations of convergence speed will depend from particular contour; • The numerical schemes of researched methods become more difficult. The singularity appears in new kernel and we are not able to use the numerical schemes of mechanical quadrature method because of a singularity for new kernel. We suggest the numerical schemes of the reduction method over the system of Faber-Laurent polynomials for the approximate solution of weakly singular integro- differential equations defined on smooth closed contours in the complex plane. We use the cut-off technique kernel to reduce the weakly singular integro- differential equation to the continuous one. Our approach is based on the Krykunov theory and Zolotarevski results. We obtain the theoretical justification in Generalized Holder spaces. Brief Biography of the Speaker: From 1996 Dr. Iurie Caraus started working at the Faculty of Mathematics and Informatics, Moldova State University. From 2004-2011, Dr. Iurie Caraus was Associate Professor at the Faculty of Mathematics and Informatics, Moldova State University, Chisinau. In 1998 he obtained PhD in Numerical Mathematics. Visiting Universities • October 2010- July 2011, Fulbright Scholar, Department of Mathematics, NC State University, Raleigh, USA; • August 2010-September 2010,University of Boudreaux1, France; • August 2009-August 2010, PostDoc, Center of Mathematics and Application, Lisbon, Portugal; • May 2007-August 2008, Visiting Researcher, Department of Computer Science, Leuven, Belgium; • April 2006-June 2006 Junior Visiting Researcher, Department of Mathematics and its Applications, Central European University, Budapest, Hungary; • February 2005- August 2005 Visiting Researcher, Department of Mathematics and Informatics, University of Trieste, Trieste, Italy; • 15.09.04-14.12.04 Visiting Researcher, Technische Universitat; • Faculty of Mathematics, Chemnitz, Germany, DAAD scholarship; • January 2003-April 2003 Visiting Researcher, NC State University, Department of Mathematics, Raleigh, USA. Fields of Scientific Interests Collocation Methods, Cauchy Singular Integral Equations, Finite Elements Methods, Optimization, Information Security, Mathematical Economics Publications: more than 40 articles in journals and proceedings, 3 didactical materials ISBN: 978-1-61804-148-7 17
Mathematical Methods for Information Science and Economics Plenary Lecture 5 Robust Video Object Detection and Tracking Techniques Professor Tudor Barbu Institute of Computer Science Romanian Academy, Iasi branch Romania E-mail: tudbar@iit.tuiasi.ro Abstract: Video object detection and tracking represents an important computer vision domain that has been vividly researched in the last decades. It has promising applications in numerous important fields, such as video compression, video surveillance, human-computer interaction, video indexing and retrieval, medical imaging, traffic monitoring, augmented reality and robotics. Obviously, it consists of two closely related processes. The first one, video object detection involves locating an image object in theframes of a video sequence, while the second one, video tracking, represents the process ofmonitoring the video object spatial and temporal changes during the moviesequence, including its presence, position, size and shape.While an object detection algorithm identifies image objects in video frames, an object tracking procedure must solve the temporal correspondence problem that is the task of matching the target object in successive frames. Numerous video detection and tracking technologies have been developed inrecent years. Object detection can be performed through various approaches, such as:region-based image segmentation, background subtraction, temporaldifferencing, active contour modelsand the generalized Hough transforms.Video tracking techniques are based on Kalman filtering, Hidden Markov Models, optical flow, template matching, mean-shift trackingand contour tracking.Objecttracking is often a time consuming process due to the amount of data contained by video streams. Also, video tracking represents a difficult process, becausevarious factors such as abrupt object motion, object occlusions or camera motion. There are various types of tracking, depending on the target object character (static or moving) and the camera (fixed or moving). We approached the object detection and tracking domain in our previous works, developing some robust detection and tracking techniques for both static camera and moving camera videos. Thus, we proposed several automatic temporal-differencing based moving object detection approaches for fixed camera video sequences. The object tracking was performed using template matching and various object featuring methods. We used HOG-based, normalized cross-correlation based and 2D Gabor filtering based features for this purpose. Also, we considered video tracking approaches which are able to track successfully both the static and moving objects, in both static-camera and moving camera videos. Thus, we developed a novel semiautomatic object tracking technique based on an improved N-Step Search algorithm and a HOG-based feature extraction. Human detection and tracking, representing an important sub-domain of object detection and tracking, is also widely approached in our research. Brief Biography of the Speaker: Dr. Tudor Barbu is currently Senior Researcher II at the Institute of Computer Science of the Romanian Academy, in Iasi, Romania. He is the coordinator of the Image and Video Processing and Analysis research collective of the institute and also member of the leading Scientific Council of this institute. Mr. Barbu has a PhD degree in Computer Science, awarded by the Faculty of Automatic Control and Computers of the University “Politehnica” of Bucharest. He has a remarkable research profile. In the last decade he published two books and four book chapters as single or main author. Also, dr. Tudor Barbu published more than 65 articles in prestigious international journals and volumes of international scientific events (conferences, symposiums and workshops). His prolific scientific activity also includes more than 35 research reports, elaborated with the institute research team coordinated by him or related to various research projects. His scientific publications have got over 70 citations, according to Google-Academic. In recent years he also coordinated various research directions in 6 projects based on contracts/grants. Dr. Tudor Barbu received also several awards for his research results, the most important being the Romanian Academy Prize “Gheorghe Cartianu”, in the Information Science and Technology domain, awarded on December 18, 2008. He is member of several conference scientific committees and also member of scientific and technical committee and editorial review boards of some journals. He is the Editor in Chief of a book. His main scientific areas of interest are: digital media (audio, video and image) signal processing and analysis, pattern recognition, computer vision, multimedia information storage, indexing and retrieval, and biometric authentication using voice, face and digital fingerprint recognition. ISBN: 978-1-61804-148-7 18
Page 2 and 3: MATHEMATICAL METHODS for INFORMATIO
Page 4: MATHEMATICAL METHODS for INFORMATIO
Page 7 and 8: Julian Pucheta Chunwei Lu Miguel An
Page 9 and 10: Mathematical Methods for Informatio
Page 17: Mathematical Methods for Informatio
Page 21: Mathematical Methods for Informatio

Contents of this book - WSEAS

Create successful ePaper yourself

Delete template?

Save as template?